Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis
In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15...
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| Format: | Article |
| Language: | English |
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Croatian Society of Chemical Engineers
2020-06-01
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| Series: | Kemija u Industriji |
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| Online Access: | http://silverstripe.fkit.hr/kui/assets/Uploads/1-355-364.pdf |
| _version_ | 1818256035275603968 |
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| author | Souad Belmadani Salah Hanini Maamar Laidi Cherif Si-Moussa Mabrouk Hamadache |
| author_facet | Souad Belmadani Salah Hanini Maamar Laidi Cherif Si-Moussa Mabrouk Hamadache |
| author_sort | Souad Belmadani |
| collection | DOAJ |
| description | In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s−1 and density at 20 °C in the range of 0.7560–0.9188 g cm−3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results. |
| first_indexed | 2024-12-12T17:21:21Z |
| format | Article |
| id | doaj.art-a4e76fde5cdc4bb8b089778d2a803f2e |
| institution | Directory Open Access Journal |
| issn | 0022-9830 1334-9090 |
| language | English |
| last_indexed | 2024-12-12T17:21:21Z |
| publishDate | 2020-06-01 |
| publisher | Croatian Society of Chemical Engineers |
| record_format | Article |
| series | Kemija u Industriji |
| spelling | doaj.art-a4e76fde5cdc4bb8b089778d2a803f2e2022-12-22T00:17:39ZengCroatian Society of Chemical EngineersKemija u Industriji0022-98301334-90902020-06-01697-835536410.15255/KUI.2019.053Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative AnalysisSouad Belmadani0Salah Hanini1Maamar Laidi2Cherif Si-Moussa3Mabrouk Hamadache4Department of Chemical Industry, University of Saad Dahlab of Blida 1, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaIn the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s−1 and density at 20 °C in the range of 0.7560–0.9188 g cm−3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results.http://silverstripe.fkit.hr/kui/assets/Uploads/1-355-364.pdfmodellingneural networkkinematic viscositydensitybiofuels |
| spellingShingle | Souad Belmadani Salah Hanini Maamar Laidi Cherif Si-Moussa Mabrouk Hamadache Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis Kemija u Industriji modelling neural network kinematic viscosity density biofuels |
| title | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis |
| title_full | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis |
| title_fullStr | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis |
| title_full_unstemmed | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis |
| title_short | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis |
| title_sort | artificial neural network models for prediction of density and kinematic viscosity of different systems of biofuels and their blends with diesel fuel comparative analysis |
| topic | modelling neural network kinematic viscosity density biofuels |
| url | http://silverstripe.fkit.hr/kui/assets/Uploads/1-355-364.pdf |
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